DRL-based Multi-UAV trajectory optimization for ultra-dense small cells

Abstract

In this paper, we propose a deep reinforcement learning (DRL) based unmanned aerial vehicles (UAV)-assisted trajectory optimization for ultra-dense small cell networks. We assume that each UAV is equipped with a sensing radio to obtain distance information to the UEs and other UAVs in the network which are used to update the UAV’s trajectory. The proposed DRL-based system selects the optimal joint control actions for the UAVs that maximizes the system sum-rate. The simulation results show that the proposed DRL-based UAV controller provides fast UAV placement in the network with a high system performance when compared with the benchmark schemes.

Keywords

• 6G
• Multiagent deep reinforcement learning (DRL)
• Multitarget tracking
• Unmanned aerial vehicle